N-glycoproteomics - an automated workflow approach.

Area of Science:

• Glycomics and Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Glycan structures are crucial for protein function, but traditional analysis methods for glycoproteins lack positional information and require extensive manual interpretation.
• Analyzing intact glycopeptides historically demanded purified proteins and complex spectral interpretation, limiting throughput and accessibility.

Purpose of the Study:

• To develop and validate an automated computational workflow for the analysis of native glycopeptide mixtures using tandem mass spectrometry.
• To enable simultaneous determination of amino acid sequences and glycan structures from intact glycopeptides without prior protein purification.

Main Methods:

• Tandem mass spectrometry was employed to analyze mixtures of native glycopeptides.
• An automated in silico workflow integrated spectral data, generated a human N-glycopeptide library, and matched spectra to theoretical fragments.
• Statistical validation using target-decoy filtering and subsequent glycan structure calculation were performed.

Main Results:

• The workflow successfully identified 62 glycopeptide spectra with validated amino acid sequences, glycan compositions, and structures from serotransferrin.
• Analysis of human plasma glycoproteins identified 80 glycopeptides, providing their glycan compositions or structures.
• The automated process reduced interpretation time from weeks/months to hours, requiring no advanced computational expertise.

Conclusions:

• The developed automated workflow significantly enhances the efficiency and accessibility of intact glycopeptide analysis.
• This approach provides comprehensive information on both amino acid and glycan moieties within complex biological mixtures.
• The method offers a powerful tool for advancing glycoprotein research in life sciences.